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Author [ Title(Desc)] Type Year
F
Zhu, M., 2019. On fitting complex models to noisy data. In Proceedings of the International Conference on Statistics: Theory and Applications. pp. 34.1–34.7. Available at: http://doi.org/10.11159/icsta19.34.
Zhu, M., 2004. On the forward and backward algorithms of projection pursuit. Annals of Statistics, 32, pp.233–244. Available at: http://doi.org/10.1214/aos/1079120135.
Hofert, M. et al., 2019. A framework for measuring association of random vectors via collapsed random variables. Journal of Multivariate Analysis, 172, pp.5–27. Available at: http://doi.org/10.1016/j.jmva.2019.02.012.
H
Wu, Y., Qin, Y. & Zhu, M., 2020. High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition. Canadian Journal of Statistics, 48, pp.308–337. Available at: http://doi.org/10.1002/cjs.11532.
Zhu, M., 2008. How to draw a trilinear plot?. ASA Statistical Computing and Graphics Newsletter, 19, pp.7–9. Available at: http://stat-computing.org/newsletter/issues/scgn-19-1.pdf.
I
Zhu, M., Wang, S. & Xin, L., 2012. On individual neutrality and collective decision making. The Mathematical Scientist, 37, pp.141–146. Available at: http://www.appliedprobability.org/data/files/TMS%20articles/37_2_8.pdf.
K
Zhu, M., 2008. Kernels and ensembles: Perspectives on statistical learning. The American Statistician, 62, pp.97–109. Available at: http://doi.org/10.1198/000313008X306367.
L
Zhu, M., Su, W. & Chipman, H.A., 2006. LAGO: A computationally efficient approach for statistical detection. Technometrics, 48, pp.193–205. Available at: http://doi.org/10.1198/004017005000000643.
Laflamme-Sanders, A. & Zhu, M., 2008. LAGO on the unit sphere. Neural Networks, 21, pp.1220–1223. Available at: http://doi.org/10.1016/j.neunet.2008.08.002.
Zhu, M. & Hastie, T.J., 2010. Letter to the editor. Journal of the American Statistical Association, 105, pp.880–881. Available at: http://doi.org/10.1198/jasa.2010.tm09295.
M
Zhu, M., 2014. Making personalized recommendations in e-commerce. In Statistics in Action: A Canadian Outlook. Chapman & Hall, pp. 259–268. Available at: http://ssc.ca/sites/default/files/data/Members/public/Publications/BookFiles/Book/259-268.pdf.
Hofert, M., Prasad, A. & Zhu, M., 2022. Multivariate time-series modeling with generative neural networks. Econometrics and Statistics, 23, pp.147–164. Available at: https://doi.org/10.1016/j.ecosta.2021.10.011.
O
P
Gu, H., Kenney, T. & Zhu, M., 2010. Partial generalized additive models: An information-theoretic approach for dealing with concurvity and selecting variables. Journal of Computational and Graphical Statistics, 19, pp.531–551. Available at: http://doi.org/10.1198/jcgs.2010.07139.
Zhu, M., 2010. Predictive analytics: Managing fundamental tradeoffs. Analytics, September-October, 2010, pp.18–21. Available at: http://viewer.zmags.com/publication/b0fd98bd#/b0fd98bd/19.
Zhang, C., Wu, Y. & Zhu, M., 2019. Pruning variable selection ensembles. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12, pp.168–184. Available at: http://doi.org/10.1002/sam.11410.
Su, W., Chipman, H.A. & Zhu, M., 2011. Pseudo-likelihood inference underestimates model uncertainty: Evidence from Bayesian nearest neighbours. Journal of the Iranian Statistical Society, 10, pp.167–180. Available at: http://jirss.irstat.ir/article-1-162-en.html.
Q
Wu, Y., Qin, Y. & Zhu, M., 2019. Quadratic discriminant analysis for high-dimensional data. Statistica Sinica, 29, pp.939–960. Available at: http://doi.org/10.5705/ss.202016.0034.

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